SoroushVahidi/scidocs huggingface dataset:
Lens on the Endpoint: Hunting for Malicious Software Through Endpoint Data Analysis
(2017)

Organizations are facing an increasing number of criminal threats ranging from opportunistic malware to more advanced targeted attacks. While various security technologies are available to protect organizations’ perimeters, still many breaches lead to undesired consequences such as loss of proprietary information, financial burden, and reputation defacing. Recently, endpoint monitoring agents that inspect system-level activities on user machines started to gain traction and be deployed in the industry as an additional defense layer. Their application, though, in most cases is only for forensic investigation to determine the root cause of an incident. In this paper, we demonstrate how endpoint monitoring can be proactively used for detecting and prioritizing suspicious software modules overlooked by other defenses. Compared to other environments in which host-based detection proved successful, our setting of a large enterprise introduces unique challenges, including the heterogeneous environment (users installing software of their choice), limited ground truth (small number of malicious software available for training), and coarse-grained data collection (strict requirements are imposed on agents’ performance overhead). Through applications of clustering and outlier detection algorithms, we develop techniques to identify modules with known malicious behavior, as well as modules impersonating popular benign applications. We leverage a large number of static, behavioral and contextual features in our algorithms, and new feature weighting methods that are resilient against missing attributes. The large majority of our findings are confirmed as malicious by anti-virus tools and manual investigation by experienced security analysts.
Distributed heterogeneous event processing: enhancing scalability and interoperability of CEP in an industrial context
(2010)

Although a significant amount of research has investigated the benefits of distributed CEP in terms of scalability and extensibility, there is an ongoing reluctance in deploying distributed CEP in an industrial context. In this paper we present the DHEP system developed together with the IBM® laboratory in Böblingen. It addresses some of the key problems in increasing the acceptance of distributed CEP, for example supporting interoperability between heterogeneous event processing systems. We present the concepts behind the DHEP system and show how those concepts help to achieve scalable and extensible event processing in an industrial context. Moreover, we verify in an evaluation study that the additional cost imposed by the DHEP system is moderate and 'affordable' for the benefits provided.
Non-iterative, feature-preserving mesh smoothing
(2003)

With the increasing use of geometry scanners to create 3D models, there is a rising need for fast and robust mesh smoothing to remove inevitable noise in the measurements. While most previous work has favored diffusion-based iterative techniques for feature-preserving smoothing, we propose a radically different approach, based on robust statistics and local first-order predictors of the surface. The robustness of our local estimates allows us to derive a non-iterative feature-preserving filtering technique applicable to arbitrary "triangle soups". We demonstrate its simplicity of implementation and its efficiency, which make it an excellent solution for smoothing large, noisy, and non-manifold meshes.
Robust real-time underwater digital video streaming using optical communication
(2013)

We present a real-time video delivery solution based on free-space optical communication for underwater applications. This solution comprises of AquaOptical II, a high-bandwidth wireless optical communication device, and a two-layer digital encoding scheme designed for error-resistant communication of high resolution images. Our system can transmit digital video reliably through a unidirectional underwater channel, with minimal infrastructural overhead. We present empirical evaluation of this system's performance for various system configurations, and demonstrate that it can deliver high quality video at up to 15 Hz, with near-negligible communication latencies of 100 ms. We further characterize the corresponding end-to-end latencies, i.e. from time of image acquisition until time of display, and reveal optimized results of under 200 ms, which facilitates a wide range of applications such as underwater robot tele-operation and interactive remote seabed monitoring.
Development of Highly Secured Cloud Rendered Log Management System
(2014)

A log is a collection of record of events that occurs within an organization containing systems and network. These logs are very important for any organization, because log file will able to record all user activities. As this log files plays vital role and also it contains sensitive information , it should be maintained highly secure. So, management and securely maintenance of log records are very tedious task. However, deploying such a system for high security and privacy of log records is an overhead for an organization and also it requires additional cost. Many techniques have been design so far for security of log records. The alternative solution is to maintaining log records over a cloud database. Log files over cloud environment leads to challenges about privacy, confidentiality and integrity of log files. In this paper, we propose highly secured cloud rendered log management and also use of some cryptographic algorithms for dealing the issues to access a cloud based data storage. To the best of knowledge, this is the strong work to provide a complete solution to the cloud based secure log management problem.
Inside help: An integrative review of champions in healthcare-related implementation
(2018)

Background/aims
The idea that champions are crucial to effective healthcare-related implementation has gained broad acceptance; yet the champion construct has been hampered by inconsistent use across the published literature. This integrative review sought to establish the current state of the literature on champions in healthcare settings and bring greater clarity to this important construct.


Methods
This integrative review was limited to research articles in peer-reviewed, English-language journals published from 1980 to 2016. Searches were conducted on the online MEDLINE database via OVID and PubMed using the keyword "champion." Several additional terms often describe champions and were also included as keywords: implementation leader, opinion leader, facilitator, and change agent. Bibliographies of full-text articles that met inclusion criteria were reviewed for additional references not yet identified via the main strategy of conducting keyword searches in MEDLINE. A five-member team abstracted all full-text articles meeting inclusion criteria.


Results
The final dataset for the integrative review consisted of 199 unique articles. Use of the term champion varied widely across the articles with respect to topic, specific job positions, or broader organizational roles. The most common method for operationalizing champion for purposes of analysis was the use of a dichotomous variable designating champion presence or absence. Four studies randomly allocated of the presence or absence of champions.


Conclusions
The number of published champion-related articles has markedly increased: more articles were published during the last two years of this review (i.e. 2015-2016) than during its first 30 years (i.e. 1980-2009).The number of champion-related articles has continued to increase sharply since the year 2000. Individual studies consistently found that champions were important positive influences on implementation effectiveness. Although few in number, the randomized trials of champions that have been conducted demonstrate the feasibility of using experimental design to study the effects of champions in healthcare.
Common criteria compliant software development (CC-CASD)
(2013)

In order to gain their customers' trust, software vendors can certify their products according to security standards, e.g., the Common Criteria (ISO 15408). However, a Common Criteria certification requires a comprehensible documentation of the software product. The creation of this documentation results in high costs in terms of time and money.
 We propose a software development process that supports the creation of the required documentation for a Common Criteria certification. Hence, we do not need to create the documentation after the software is built. Furthermore, we propose to use an enhanced version of the requirements-driven software engineering process called ADIT to discover possible problems with the establishment of Common Criteria documents. We aim to detect these issues before the certification process. Thus, we avoid expensive delays of the certification effort. ADIT provides a seamless development approach that allows consistency checks between different kinds of UML models. ADIT also supports traceability from security requirements to design documents. We illustrate our approach with the development of a smart metering gateway system.
Learning influence probabilities in social networks
(2010)

Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a social graph with edges labeled with probabilities of influence between users. However, the question of where these probabilities come from or how they can be computed from real social network data has been largely ignored until now. Thus it is interesting to ask whether from a social graph and a log of actions by its users, one can build models of influence. This is the main problem attacked in this paper. In addition to proposing models and algorithms for learning the model parameters and for testing the learned models to make predictions, we also develop techniques for predicting the time by which a user may be expected to perform an action. We validate our ideas and techniques using the Flickr data set consisting of a social graph with 1.3M nodes, 40M edges, and an action log consisting of 35M tuples referring to 300K distinct actions. Beyond showing that there is genuine influence happening in a real social network, we show that our techniques have excellent prediction performance.
Novelty and redundancy detection in adaptive filtering
(2002)

This paper addresses the problem of extending an adaptive information filtering system to make decisions about the novelty and redundancy of relevant documents. It argues that relevance and redundance should each be modelled explicitly and separately. A set of five redundancy measures are proposed and evaluated in experiments with and without redundancy thresholds. The experimental results demonstrate that the cosine similarity metric and a redundancy measure based on a mixture of language models are both effective for identifying redundant documents.
Collaborative location and activity recommendations with GPS history data
(2010)

With the increasing popularity of location-based services, such as tour guide and location-based social network, we now have accumulated many location data on the Web. In this paper, we show that, by using the location data based on GPS and users' comments at various locations, we can discover interesting locations and possible activities that can be performed there for recommendations. Our research is highlighted in the following location-related queries in our daily life: 1) if we want to do something such as sightseeing or food-hunting in a large city such as Beijing, where should we go? 2) If we have already visited some places such as the Bird's Nest building in Beijing's Olympic park, what else can we do there? By using our system, for the first question, we can recommend her to visit a list of interesting locations such as Tiananmen Square, Bird's Nest, etc. For the second question, if the user visits Bird's Nest, we can recommend her to not only do sightseeing but also to experience its outdoor exercise facilities or try some nice food nearby. To achieve this goal, we first model the users' location and activity histories that we take as input. We then mine knowledge, such as the location features and activity-activity correlations from the geographical databases and the Web, to gather additional inputs. Finally, we apply a collective matrix factorization method to mine interesting locations and activities, and use them to recommend to the users where they can visit if they want to perform some specific activities and what they can do if they visit some specific places. We empirically evaluated our system using a large GPS dataset collected by 162 users over a period of 2.5 years in the real-world. We extensively evaluated our system and showed that our system can outperform several state-of-the-art baselines.
A mobile application framework for the geospatial web
(2007)

In this paper we present an application framework that leverages geospatial content on the World Wide Web by enabling innovative modes of interaction and novel types of user interfaces on advanced mobile phones and PDAs. We discuss the current development steps involved in building mobile geospatial Web applications and derive three technological pre-requisites for our framework: spatial query operations based on visibility and field of view, a 2.5D environment model, and a presentationindependent data exchange format for geospatial query results. We propose the Local Visibility Model as a suitable XML-based candidate and present a prototype implementation.
Mining interesting locations and travel sequences from GPS trajectories
(2009)

The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.
A Framework for Evaluating Intrusion Detection Architectures in Advanced Metering Infrastructures
(2014)

The scale and complexity of Advanced Metering Infrastructure (AMI) networks requires careful planning for the deployment of security solutions. In particular, the large number of AMI devices and the volume and diversity of communication expected to take place on the various AMI networks make the role of intrusion detection systems (IDSes) critical. Understanding the trade-offs for a scalable and comprehensive IDS is key to investing in the right technology and deploying sensors at optimal locations. This paper reviews the benefits and costs associated with different IDS deployment options, including either centralized or distributed solution. A general cost-model framework is proposed to help utilities (AMI asset owners) make more informed decisions when selecting IDS deployment architectures and managing their security investments. We illustrate how the framework can be applied through case studies, and highlight the interesting cost/benefit trade-offs that emerge.
Interactive volumetric fog display
(2015)

Traditional fog screens are 2D. We propose a new design of fog screen that can generate fog at different depth positions and at only where necessary. Our approach applies projection mapping onto a non-planar and reconfigurable fog screen, thus enabling interactive visual contents to be displayed at multiple depth levels. Viewers can perceive the three-dimensionality naturally, and interact with the unencumbered images by touching them directly in mid-air. The display can be used in mixed reality settings where physical objects co-exist and interact with the 3D imagery in physical space.
Adversarial Reinforcement Learning in a Cyber Security Simulation
(2017)

This paper focuses on cyber-security simulations in networks modeled as a Markov game with incomplete information and stochastic elements. The resulting game is an adversarial sequential decision making problem played with two agents, the attacker and defender. The two agents pit one reinforcement learning technique, like neural networks, Monte Carlo learning and Q-learning, against each other and examine their effectiveness against learning opponents. The results showed that Monte Carlo learning with the Softmax exploration strategy is most effective in performing the defender role and also for learning attacking strategies.
SALMA: Standard Arabic Language Morphological Analysis
(2013)

Morphological analyzers are preprocessors for text analysis. Many Text Analytics applications need them to perform their tasks. This paper reviews the SALMA-Tools (Standard Arabic Language Morphological Analysis) [1]. The SALMA-Tools is a collection of open-source standards, tools and resources that widen the scope of Arabic word structure analysis - particularly morphological analysis, to process Arabic text corpora of different domains, formats and genres, of both vowelized and non-vowelized text. Tag-assignment is significantly more complex for Arabic than for many languages. The morphological analyzer should add the appropriate linguistic information to each part or morpheme of the word (proclitic, prefix, stem, suffix and enclitic); in effect, instead of a tag for a word, we need a subtag for each part. Very fine-grained distinctions may cause problems for automatic morphosyntactic analysis - particularly probabilistic taggers which require training data, if some words can change grammatical tag depending on function and context; on the other hand, fine-grained distinctions may actually help to disambiguate other words in the local context. The SALMA - Tagger is a fine grained morphological analyzer which is mainly depends on linguistic information extracted from traditional Arabic grammar books and prior-knowledge broad-coverage lexical resources; the SALMA - ABCLexicon. More fine-grained tag sets may be more appropriate for some tasks. The SALMA - Tag Set is a standard tag set for encoding, which captures long-established traditional fine-grained morphological features of Arabic, in a notation format intended to be compact yet transparent.
Empirical evidence in global software engineering: a systematic review
(2009)

Recognized as one of the trends of the 21st century, globalization of the world economies brought significant changes to nearly all industries, and in particular it includes software development. Many companies started global software engineering (GSE) to benefit from cheaper, faster and better development of software systems, products and services. However, empirical studies indicate that achieving these benefits is not an easy task. Here, we report our findings from investigating empirical evidence in GSE-related research literature. By conducting a systematic review we observe that the GSE field is still immature. The amount of empirical studies is relatively small. The majority of the studies represent problem-oriented reports focusing on different aspects of GSE management rather than in-depth analysis of solutions for example in terms of useful practices or techniques. Companies are still driven by cost reduction strategies, and at the same time, the most frequently discussed recommendations indicate a necessity of investments in travelling and socialization. Thus, at the same time as development goes global there is an ambition to minimize geographical, temporal and cultural separation. These are normally integral parts of cross-border collaboration. In summary, the systematic review results in several descriptive classifications of the papers on empirical studies in GSE and also reports on some best practices identified from literature.
Fundamental limits of RSS fingerprinting based indoor localization
(2015)

Indoor localization has been an active research field for decades, where the received signal strength (RSS) fingerprinting based methodology is widely adopted and induces many important localization techniques such as the recently proposed one building the fingerprint database with crowd-sourcing. While efforts have been dedicated to improve the accuracy and efficiency of localization, the fundamental limits of RSS fingerprinting based methodology itself is still unknown in a theoretical perspective. In this paper, we present a general probabilistic model to shed light on a fundamental question: how good the RSS fingerprinting based indoor localization can achieve? Concretely, we present the probability that a user can be localized in a region with certain size, given the RSS fingerprints submitted to the system. We reveal the interaction among the localization accuracy, the reliability of location estimation and the number of measurements in the RSS fingerprinting based location determination. Moreover, we present the optimal fingerprints reporting strategy that can achieve the best accuracy for given reliability and the number of measurements, which provides a design guideline for the RSS fingerprinting based indoor localization facilitated by crowdsourcing paradigm.
Human ES cell-derived neural rosettes reveal a functionally distinct early neural stem cell stage.
(2008)

Neural stem cells (NSCs) yield both neuronal and glial progeny, but their differentiation potential toward multiple region-specific neuron types remains remarkably poor. In contrast, embryonic stem cell (ESC) progeny readily yield region-specific neuronal fates in response to appropriate developmental signals. Here we demonstrate prospective and clonal isolation of neural rosette cells (termed R-NSCs), a novel NSC type with broad differentiation potential toward CNS and PNS fates and capable of in vivo engraftment. R-NSCs can be derived from human and mouse ESCs or from neural plate stage embryos. While R-NSCs express markers classically associated with NSC fate, we identified a set of genes that specifically mark the R-NSC state. Maintenance of R-NSCs is promoted by activation of SHH and Notch pathways. In the absence of these signals, R-NSCs rapidly lose rosette organization and progress to a more restricted NSC stage. We propose that R-NSCs represent the first characterized NSC stage capable of responding to patterning cues that direct differentiation toward region-specific neuronal fates. In addition, the R-NSC-specific genetic markers presented here offer new tools for harnessing the differentiation potential of human ESCs.